This section investigates the effect of different sources of class imbalance within a few-shot learning task. Namely, a task can contain an unbalanced support set as well as the query set. Although in the main paper body we examined the problem of an imbalanced support sets, in this section, we investigate whether imbalanced query set in the meta-training task affects generalization to imbalanced support set tasks at test time. We reason that since MAML-like algorithms often involve second-order derivative approximations, the algorithm is not directly influenced by imbalance at support set level. Metric-learning algorithms such as ProtoNet also optimize on the query rather than the support set loss. Thus, imbalance in the query set has a direct effect on the optimization of meta-learners. This section investigates four meta-training settings, along two axes: 1) balanced vs imbalanced, and 2) support vs query set. Here, we define imbalanced tasks as having 1-9 shot/queries linear imbalance in the support/query set and 5shot/query in the balanced equivalent. At meta-test time, all methods are evaluated on tasks with imbalanced support sets and balanced query sets; we want to investigate whether an imbalanced query set during meta-training affects the imbalanced support set task during evaluation and equally care about all classes. Table REF  shows the performance of algorithms with balanced and imbalanced query sets for selected algorithms. The results show that having the query set balanced during meta-learning is generally preferred by metric-based methods; however, the trend is not so clear for optimization-based algorithms. Future work could investigate the effect of query set imbalance on transductive inference methods [1]}, [2]}, [3]}, [4]}.
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